IoT-Driven Water Leak Detection: A Comparative Study of Machine Learning and Deep Learning Models for Time-Series Sensor Data with Explainable AI

IoT-Driven Water Leak Detection: A Comparative Study of Machine Learning and Deep Learning Models for Time-Series Sensor Data with Explainable AI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IoT-Driven Water Leak Detection: A Comparative Study of Machine Learning and Deep Learning Models for Time-Series Sensor Data with Explainable AI Md. Morshed Ali, Emran Ahmed, Kashfi Shormita Kushal, Md Ashraf Uddin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8184331/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Urban water distribution systems have been a major threat to the preservation of world freshwater resources due to inefficiencies as large amounts of treated water end up being wasted each year by leakages and bursting pipes. Conventional leak detection techniques frequently encounter difficulties, including a significant dependence on the human selection and black box approaches, which can be labor-intensive and less efficient. This paper will manage this obstacle by suggesting an effective, evidence-based framework of early and accurate detection of pipeline failures. The paradigm of Internet of Things is proposed to gather a multivariate time-series data which is used for multi-classification to make a distinction between the normal state, leak state, and burst state. With a systematic comparative study assessing the performance of six light-weight classical Machine Learning (ML) algorithms against a Deep Learning (DL), experiment results show that Random Forest algorithm exhibit optimal performance, reaching a test accuracy and an F1-score of 0.99, which is able to cope with the enormous class imbalance of the data. To overcome the traditional black-box constraint of AI models with great performance and develop operational trust, this study incorporates an overall XAI framework. The results of this XAI analysis are clear and practical, and quantitative demonstrating that the pressure is the most significant predictive variable. The paper finds that a synergistic combination of high-accuracy tree-based model with XAI approach is a viable, and operationally feasible way forward towards integrating into the proactive management of critical water infrastructure, thus adding to the achievement of improved urban water security. Water Leak Detection Smart Water Infrastructure Internet of Things (IoT) Machine Learning Deep Learning Explainable AI (XAI) Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 23 Mar, 2026 Reviewers agreed at journal 12 Dec, 2025 Reviewers invited by journal 12 Dec, 2025 Editor assigned by journal 12 Dec, 2025 First submitted to journal 11 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8184331","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559465017,"identity":"41dc282e-fb30-4faa-aed9-5d089173ad7d","order_by":0,"name":"Md. 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